Prepare data
Prevalence
df_ger_prev <- read_csv('Germany_timeseries_prep.csv')
Parsed with column specification:
cols(
date = [31mcol_character()[39m,
anzahlfall = [32mcol_double()[39m,
kreis = [32mcol_double()[39m,
ewz = [32mcol_double()[39m,
shape__area = [32mcol_double()[39m,
cumcase = [32mcol_double()[39m,
kreis_name = [31mcol_character()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
open = [32mcol_double()[39m,
rate_day = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
runday = [32mcol_double()[39m
)
df_ger_prev <- df_ger_prev %>% mutate(date = as.Date(date, "%d%b%Y"),
kreis = as.character(kreis)) %>%
dplyr::select(kreis, date, rate_day)
df_ger_prev
Scoial distancing
df_ger_socdist <- read_csv('Germany_socdist_fb_kreis.csv')
Parsed with column specification:
cols(
all_day_ratio_single_tile_users = [32mcol_double()[39m,
date = [34mcol_date(format = "")[39m,
kreis = [32mcol_double()[39m,
kreis_name = [31mcol_character()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
open = [32mcol_double()[39m,
frequ = [32mcol_double()[39m,
runday = [32mcol_double()[39m
)
df_ger_socdist <- df_ger_socdist %>%
rename(socdist_single_tile = all_day_ratio_single_tile_users) %>%
select(kreis, date, socdist_single_tile) %>%
mutate(kreis = as.character(kreis))
df_ger_socdist
NA
Personality
df_ger_pers <- read_csv('Germany_timeseries_prep.csv')
Parsed with column specification:
cols(
date = [31mcol_character()[39m,
anzahlfall = [32mcol_double()[39m,
kreis = [32mcol_double()[39m,
ewz = [32mcol_double()[39m,
shape__area = [32mcol_double()[39m,
cumcase = [32mcol_double()[39m,
kreis_name = [31mcol_character()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
open = [32mcol_double()[39m,
rate_day = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
runday = [32mcol_double()[39m
)
df_ger_pers <- df_ger_pers %>%
select(kreis, open, sci, extra, agree, neuro) %>%
dplyr::rename(pers_o = open,
pers_c = sci,
pers_e = extra,
pers_a = agree,
pers_n = neuro) %>%
distinct() %>%
mutate(kreis = as.character(kreis))
df_ger_pers
NA
Controls
df_ger_ctrl <- read.csv2('Germany_controls.csv', sep = ';', dec=',')
df_ger_ctrl <- df_ger_ctrl %>% select(-kreis_nme) %>%
mutate(kreis = as.character(kreis),
popdens = popdens %>%
as.character() %>%
str_replace('\\.', '')%>%
as.numeric())
df_ger_ctrl
NA
Merge prevalence data
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_prev$date),
max(df_ger_prev$date), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge prevalence data
df_ger_prev <- df_ger_prev %>%
inner_join(df_ger_pers, by = 'kreis') %>%
inner_join(df_ger_ctrl, by = 'kreis') %>%
merge(df_dates, by='date') %>%
arrange(kreis)
df_ger_prev
Merge socdist data
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_socdist$date),
max(df_ger_socdist$date), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge socdist data
df_ger_socdist <- df_ger_socdist %>%
inner_join(df_ger_pers, by = 'kreis') %>%
inner_join(df_ger_ctrl, by = 'kreis') %>%
merge(df_dates, by='date') %>%
arrange(kreis)
df_ger_socdist
NA
Control for weekend effect
easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)
df_ger_loess <- df_ger_socdist %>%
mutate(weekday = format(date, '%u')) %>%
filter(!(weekday %in% c('6','7') | date %in% easter)) %>%
split(.$kreis) %>%
map(~ loess(socdist_single_tile ~ time, data = .)) %>%
map(predict, 1:max(df_ger_socdist$time)) %>%
bind_rows() %>%
gather(key = 'kreis', value = 'loess') %>%
group_by(kreis) %>%
mutate(time = row_number())
df_ger_socdist <- df_ger_socdist %>% merge(df_ger_loess, by=c('kreis', 'time')) %>%
mutate(weekday = format(date, '%u')) %>%
mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter,
loess, socdist_single_tile)) %>%
arrange(kreis, time) %>%
select(-weekday)
df_ger_socdist %>% drop_na()
NA
Explore data
Plot prevalence over time
df_ger_prev %>% ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_ger_prev %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





Plot social distancing (single tile) over time
df_ger_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_ger_socdist %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





df_ger_socdist <- df_ger_socdist %>% mutate(socdist_single_tile = socdist_single_tile_clean) %>%
select(-loess, -socdist_single_tile_clean)
Correlations
df_ger_prev %>% group_by(kreis) %>%
summarize_if(is.numeric, mean) %>%
select(-kreis, -time) %>%
cor(use='pairwise.complete') %>%
round(3) %>% as.data.frame()
df_ger_socdist %>% group_by(kreis) %>%
summarize_if(is.numeric, mean) %>%
select(-kreis, -time) %>%
cor(use='pairwise.complete') %>%
round(3) %>% as.data.frame()
NA
NA
Rescale data
lvl2_scaled <- df_ger_prev %>%
dplyr::select(-time, -rate_day, -date) %>%
distinct() %>%
mutate_at(vars(-kreis), scale)
lvl1_scaled <- df_ger_prev %>% select(kreis, time, rate_day)
df_ger_prev_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')
df_ger_prev_scaled
NA
lvl2_scaled <- df_ger_socdist %>%
dplyr::select(-time, -socdist_single_tile, -date) %>%
distinct() %>%
mutate_at(vars(-kreis), scale)
lvl1_scaled <- df_ger_socdist %>% select(kreis, time, socdist_single_tile)
df_ger_socdist_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')
df_ger_socdist_scaled
NA
Predict Prevalence
Explore distributions
df_ger_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()

df_ger_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()

Predict COVID onset with time-to-event regression
# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_onset_prev)
cox_onset_prev %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n, data = df_ger_onset_prev)
n= 400, number of events= 400
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.131653 1.140712 0.051953 2.534 0.0113 *
pers_c -0.104500 0.900775 0.053314 -1.960 0.0500 *
pers_e 0.056186 1.057794 0.052903 1.062 0.2882
pers_a -0.008624 0.991413 0.049615 -0.174 0.8620
pers_n -0.276799 0.758207 0.057728 -4.795 1.63e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1407 0.8766 1.0303 1.263
pers_c 0.9008 1.1102 0.8114 1.000
pers_e 1.0578 0.9454 0.9536 1.173
pers_a 0.9914 1.0087 0.8995 1.093
pers_n 0.7582 1.3189 0.6771 0.849
Concordance= 0.592 (se = 0.018 )
Likelihood ratio test= 39.92 on 5 df, p=2e-07
Wald test = 37.99 on 5 df, p=4e-07
Score (logrank) test = 37.99 on 5 df, p=4e-07
# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_onset_prev)
cox_onset_prev_ctrl %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n + women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens,
data = df_ger_onset_prev)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.060631 1.062506 0.059458 1.020 0.307861
pers_c -0.082813 0.920523 0.060718 -1.364 0.172598
pers_e 0.009496 1.009541 0.059390 0.160 0.872972
pers_a -0.019747 0.980447 0.057978 -0.341 0.733413
pers_n -0.249887 0.778889 0.064236 -3.890 0.000100 ***
women 0.083662 1.087261 0.068554 1.220 0.222322
academics 0.335157 1.398160 0.100092 3.348 0.000813 ***
cdu 0.179656 1.196805 0.079129 2.270 0.023182 *
afd -0.017508 0.982644 0.079368 -0.221 0.825408
hospital_beds -0.233707 0.791594 0.068731 -3.400 0.000673 ***
tourism_beds 0.055041 1.056584 0.055716 0.988 0.323208
gdp -0.151600 0.859332 0.115313 -1.315 0.188618
manufact 0.112626 1.119214 0.095688 1.177 0.239190
airport -0.179572 0.835627 0.062056 -2.894 0.003807 **
age -0.102691 0.902406 0.090956 -1.129 0.258890
popdens 0.125177 1.133349 0.087247 1.435 0.151360
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0625 0.9412 0.9456 1.1938
pers_c 0.9205 1.0863 0.8172 1.0369
pers_e 1.0095 0.9905 0.8986 1.1342
pers_a 0.9804 1.0199 0.8751 1.0984
pers_n 0.7789 1.2839 0.6867 0.8834
women 1.0873 0.9197 0.9506 1.2436
academics 1.3982 0.7152 1.1491 1.7012
cdu 1.1968 0.8356 1.0249 1.3976
afd 0.9826 1.0177 0.8411 1.1480
hospital_beds 0.7916 1.2633 0.6918 0.9057
tourism_beds 1.0566 0.9464 0.9473 1.1785
gdp 0.8593 1.1637 0.6855 1.0772
manufact 1.1192 0.8935 0.9278 1.3501
airport 0.8356 1.1967 0.7399 0.9437
age 0.9024 1.1081 0.7551 1.0785
popdens 1.1333 0.8823 0.9552 1.3447
Concordance= 0.659 (se = 0.016 )
Likelihood ratio test= 106.5 on 16 df, p=2e-15
Wald test = 104.3 on 16 df, p=5e-15
Score (logrank) test = 108.4 on 16 df, p=9e-16
Predict prevalence slopes with linear models
# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_slope_prev)
lm_slope_prev %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_ger_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-0.032829 -0.015670 -0.005169 0.008046 0.235669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0276072 0.0013833 19.957 <2e-16 ***
pers_o -0.0008705 0.0014835 -0.587 0.558
pers_c -0.0016391 0.0015871 -1.033 0.302
pers_e 0.0021386 0.0015724 1.360 0.175
pers_a 0.0024992 0.0015982 1.564 0.119
pers_n 0.0016993 0.0016343 1.040 0.299
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02738 on 386 degrees of freedom
Multiple R-squared: 0.01215, Adjusted R-squared: -0.0006472
F-statistic: 0.9494 on 5 and 386 DF, p-value: 0.4489
# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_slope_prev)
lm_slope_prev_ctrl %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens,
data = df_ger_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-0.047064 -0.014053 -0.002229 0.008977 0.217515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0274783 0.0013022 21.101 < 2e-16 ***
pers_o -0.0003747 0.0016078 -0.233 0.8159
pers_c -0.0001953 0.0015584 -0.125 0.9004
pers_e 0.0013162 0.0015344 0.858 0.3915
pers_a 0.0012651 0.0015783 0.802 0.4233
pers_n 0.0008380 0.0015918 0.526 0.5989
women 0.0046402 0.0019204 2.416 0.0162 *
academics 0.0005925 0.0025926 0.229 0.8194
cdu 0.0096133 0.0020618 4.663 4.35e-06 ***
afd 0.0026729 0.0020254 1.320 0.1877
hospital_beds 0.0026756 0.0017517 1.527 0.1275
tourism_beds -0.0023895 0.0014941 -1.599 0.1106
gdp 0.0005674 0.0030072 0.189 0.8504
manufact 0.0028811 0.0024162 1.192 0.2339
airport 0.0033653 0.0016388 2.054 0.0407 *
age -0.0022077 0.0025050 -0.881 0.3787
popdens -0.0003063 0.0022367 -0.137 0.8911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02575 on 375 degrees of freedom
Multiple R-squared: 0.1509, Adjusted R-squared: 0.1146
F-statistic: 4.164 on 16 and 375 DF, p-value: 2.01e-07
CRF predicting slopes
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_slope_prev <- cforest(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_slope_prev,
controls = ctrls)
crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)
crf_slope_prev_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Predict Social Distancing
Change point analysis
# keep only counties with full data
kreis_complete <- df_ger_socdist_scaled %>%
group_by(kreis) %>%
summarize(n = n()) %>%
filter(n==max(.$n)) %>%
.$kreis
# run changepoint analysis
df_ger_socdist_cpt_results <- df_ger_socdist_scaled %>%
select(kreis, socdist_single_tile) %>%
filter(kreis %in% kreis_complete) %>%
split(.$kreis) %>%
map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
#penalty = 'Asymptotic',
class=TRUE,
param.estimates=TRUE,
Q=1,
test.stat = 'Normal'))
# calculate change point
df_ger_socdist_cpt_day <- df_ger_socdist_cpt_results %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_socdist = '.') %>%
rownames_to_column('kreis')
# calculate mean differences
df_ger_socdist_cpt_mean_diff <- df_ger_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_socdist = '.') %>%
rownames_to_column('kreis')
# calculate varaince differences
df_ger_socdist_cpt_var_diff <- df_ger_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$variance) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(var_diff_socdist = '.') %>%
rownames_to_column('kreis')
# merge with county data
df_ger_cpt_socdist <- df_ger_socdist_scaled %>%
select(-time, -socdist_single_tile) %>%
distinct() %>%
left_join(df_ger_socdist_cpt_day, by='kreis') %>%
left_join(df_ger_socdist_cpt_mean_diff, by='kreis') %>%
left_join(df_ger_socdist_cpt_var_diff, by='kreis') %>%
left_join(select(df_ger_onset_prev, kreis, onset_prev), by='kreis') %>%
left_join(select(df_ger_slope_prev, kreis, slope_prev), by='kreis')
# handle censored data
df_ger_cpt_socdist <- df_ger_cpt_socdist %>%
mutate(cpt_day_socdist = ifelse(is.na(cpt_day_socdist),
as.numeric(diff(range(df_ger_socdist$date))),
cpt_day_socdist)) %>%
mutate(event = ifelse(cpt_day_socdist >= 60, 0, 1))
df_ger_cpt_socdist$cpt_day_socdist %>% hist()

df_ger_cpt_socdist$mean_diff_socdist %>% hist()

df_ger_cpt_socdist$var_diff_socdist %>% hist()

for(i in head(df_ger_socdist_cpt_results, 5)){
plot(i)
}




NA

Predicting change points with time-to-event regression
# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_cpt_socdist)
cox_cpt_socdist %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n, data = df_ger_cpt_socdist)
n= 400, number of events= 400
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.13238 1.14154 0.05555 2.383 0.0172 *
pers_c -0.04634 0.95472 0.05364 -0.864 0.3876
pers_e -0.08928 0.91459 0.05443 -1.640 0.1009
pers_a -0.05236 0.94898 0.04881 -1.073 0.2833
pers_n -0.08884 0.91499 0.05376 -1.653 0.0984 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1415 0.876 1.0238 1.273
pers_c 0.9547 1.047 0.8594 1.061
pers_e 0.9146 1.093 0.8221 1.018
pers_a 0.9490 1.054 0.8624 1.044
pers_n 0.9150 1.093 0.8235 1.017
Concordance= 0.595 (se = 0.024 )
Likelihood ratio test= 10.81 on 5 df, p=0.06
Wald test = 10.76 on 5 df, p=0.06
Score (logrank) test = 10.71 on 5 df, p=0.06
# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_cpt_socdist,)
cox_cpt_socdist_ctrl %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + women + academics + cdu + afd +
hospital_beds + tourism_beds + gdp + manufact + airport +
age + popdens, data = df_ger_cpt_socdist)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.013954 1.014052 0.064817 0.215 0.82954
pers_c -0.035378 0.965241 0.061566 -0.575 0.56554
pers_e -0.157296 0.854451 0.061888 -2.542 0.01103 *
pers_a -0.008478 0.991558 0.057259 -0.148 0.88229
pers_n -0.043137 0.957780 0.060730 -0.710 0.47751
women 0.084290 1.087945 0.076682 1.099 0.27167
academics -0.055918 0.945616 0.109764 -0.509 0.61044
cdu -0.135885 0.872943 0.083444 -1.628 0.10343
afd -0.105132 0.900205 0.090520 -1.161 0.24547
hospital_beds -0.196237 0.821818 0.065813 -2.982 0.00287 **
tourism_beds 0.102105 1.107500 0.057115 1.788 0.07382 .
gdp 0.135235 1.144806 0.126125 1.072 0.28362
manufact -0.125882 0.881719 0.093401 -1.348 0.17774
airport -0.074355 0.928342 0.065237 -1.140 0.25438
age -0.181020 0.834419 0.098405 -1.840 0.06584 .
popdens 0.135757 1.145404 0.087636 1.549 0.12136
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0141 0.9861 0.8931 1.1514
pers_c 0.9652 1.0360 0.8555 1.0890
pers_e 0.8545 1.1703 0.7568 0.9646
pers_a 0.9916 1.0085 0.8863 1.1093
pers_n 0.9578 1.0441 0.8503 1.0788
women 1.0879 0.9192 0.9361 1.2644
academics 0.9456 1.0575 0.7626 1.1726
cdu 0.8729 1.1455 0.7412 1.0280
afd 0.9002 1.1109 0.7539 1.0750
hospital_beds 0.8218 1.2168 0.7224 0.9350
tourism_beds 1.1075 0.9029 0.9902 1.2387
gdp 1.1448 0.8735 0.8941 1.4659
manufact 0.8817 1.1341 0.7342 1.0588
airport 0.9283 1.0772 0.8169 1.0550
age 0.8344 1.1984 0.6881 1.0119
popdens 1.1454 0.8731 0.9646 1.3600
Concordance= 0.703 (se = 0.021 )
Likelihood ratio test= 64.83 on 16 df, p=8e-08
Wald test = 68.61 on 16 df, p=2e-08
Score (logrank) test = 70.59 on 16 df, p=8e-09
# predict hazard from personality with controls
cox_cpt_socdist_ctrl2 <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist)
cox_cpt_socdist_ctrl2 %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + women + academics + cdu + afd +
hospital_beds + tourism_beds + gdp + manufact + airport +
age + popdens + onset_prev + slope_prev, data = df_ger_cpt_socdist)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.011438 1.011504 0.065327 0.175 0.86100
pers_c -0.035315 0.965301 0.061681 -0.573 0.56695
pers_e -0.164389 0.848412 0.062459 -2.632 0.00849 **
pers_a -0.001916 0.998086 0.058253 -0.033 0.97377
pers_n -0.026007 0.974329 0.061798 -0.421 0.67388
women 0.095528 1.100239 0.078771 1.213 0.22524
academics -0.083599 0.919800 0.111618 -0.749 0.45388
cdu -0.148737 0.861796 0.087875 -1.693 0.09053 .
afd -0.092155 0.911964 0.091530 -1.007 0.31402
hospital_beds -0.191478 0.825738 0.066089 -2.897 0.00376 **
tourism_beds 0.103020 1.108513 0.057201 1.801 0.07170 .
gdp 0.171296 1.186842 0.127477 1.344 0.17903
manufact -0.151711 0.859237 0.094398 -1.607 0.10802
airport -0.064266 0.937756 0.065954 -0.974 0.32985
age -0.173100 0.841053 0.099696 -1.736 0.08251 .
popdens 0.121096 1.128733 0.087691 1.381 0.16730
onset_prev -0.008623 0.991414 0.005259 -1.640 0.10110
slope_prev 0.259984 1.296909 2.375477 0.109 0.91285
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0115 0.9886 0.88994 1.1497
pers_c 0.9653 1.0359 0.85538 1.0893
pers_e 0.8484 1.1787 0.75066 0.9589
pers_a 0.9981 1.0019 0.89040 1.1188
pers_n 0.9743 1.0263 0.86318 1.0998
women 1.1002 0.9089 0.94284 1.2839
academics 0.9198 1.0872 0.73907 1.1447
cdu 0.8618 1.1604 0.72545 1.0238
afd 0.9120 1.0965 0.76220 1.0912
hospital_beds 0.8257 1.2110 0.72542 0.9399
tourism_beds 1.1085 0.9021 0.99095 1.2400
gdp 1.1868 0.8426 0.92445 1.5237
manufact 0.8592 1.1638 0.71410 1.0339
airport 0.9378 1.0664 0.82404 1.0672
age 0.8411 1.1890 0.69177 1.0226
popdens 1.1287 0.8859 0.95049 1.3404
onset_prev 0.9914 1.0087 0.98125 1.0017
slope_prev 1.2969 0.7711 0.01233 136.4328
Concordance= 0.704 (se = 0.021 )
Likelihood ratio test= 68.07 on 18 df, p=1e-07
Wald test = 71.78 on 18 df, p=2e-08
Score (logrank) test = 73.73 on 18 df, p=1e-08
Linear models predicting mean differences
lm_meandiff_socdist <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_cpt_socdist)
lm_meandiff_socdist %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-0.042484 -0.009668 -0.000179 0.008346 0.062165
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0949484 0.0007323 129.654 < 2e-16 ***
pers_o 0.0056844 0.0007839 7.251 2.21e-12 ***
pers_c -0.0008564 0.0008316 -1.030 0.3037
pers_e 0.0008648 0.0008356 1.035 0.3014
pers_a -0.0002079 0.0008311 -0.250 0.8026
pers_n -0.0020169 0.0008627 -2.338 0.0199 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01465 on 394 degrees of freedom
Multiple R-squared: 0.1631, Adjusted R-squared: 0.1525
F-statistic: 15.36 on 5 and 394 DF, p-value: 8.43e-14
lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_cpt_socdist,)
lm_meandiff_socdist_ctrl %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens,
data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-0.045968 -0.007500 -0.000700 0.006643 0.039513
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.485e-02 5.814e-04 163.137 < 2e-16 ***
pers_o 1.708e-03 7.178e-04 2.379 0.017838 *
pers_c 7.003e-04 6.958e-04 1.007 0.314809
pers_e -8.269e-04 6.850e-04 -1.207 0.228176
pers_a -3.770e-04 7.047e-04 -0.535 0.592923
pers_n -2.724e-05 7.107e-04 -0.038 0.969449
women 2.286e-03 8.574e-04 2.666 0.008013 **
academics 1.987e-03 1.157e-03 1.716 0.086908 .
cdu 2.873e-03 9.205e-04 3.121 0.001942 **
afd -1.348e-03 9.043e-04 -1.490 0.137006
hospital_beds -2.819e-03 7.821e-04 -3.604 0.000355 ***
tourism_beds 4.121e-05 6.670e-04 0.062 0.950768
gdp 5.408e-03 1.343e-03 4.028 6.82e-05 ***
manufact -1.267e-03 1.079e-03 -1.175 0.240741
airport -1.163e-03 7.317e-04 -1.589 0.112832
age -4.297e-03 1.118e-03 -3.842 0.000143 ***
popdens 7.862e-04 9.986e-04 0.787 0.431608
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0115 on 375 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: 0.5001, Adjusted R-squared: 0.4787
F-statistic: 23.44 on 16 and 375 DF, p-value: < 2.2e-16
lm_meandiff_socdist_ctrl2 <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist)
lm_meandiff_socdist_ctrl2 %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens +
onset_prev + slope_prev, data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-0.044599 -0.007374 -0.000808 0.006306 0.040989
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.476e-02 3.013e-03 31.446 < 2e-16 ***
pers_o 1.742e-03 7.021e-04 2.481 0.013540 *
pers_c 7.559e-04 6.818e-04 1.109 0.268258
pers_e -9.932e-04 6.721e-04 -1.478 0.140321
pers_a -4.823e-04 6.900e-04 -0.699 0.485022
pers_n -2.376e-05 7.018e-04 -0.034 0.973012
women 1.794e-03 8.465e-04 2.119 0.034720 *
academics 1.794e-03 1.142e-03 1.571 0.117084
cdu 1.747e-03 9.532e-04 1.833 0.067665 .
afd -1.604e-03 8.864e-04 -1.809 0.071255 .
hospital_beds -3.013e-03 7.706e-04 -3.910 0.000109 ***
tourism_beds 3.179e-04 6.567e-04 0.484 0.628597
gdp 5.439e-03 1.317e-03 4.131 4.47e-05 ***
manufact -1.618e-03 1.060e-03 -1.526 0.127771
airport -1.478e-03 7.197e-04 -2.054 0.040649 *
age -4.000e-03 1.099e-03 -3.640 0.000312 ***
popdens 7.496e-04 9.795e-04 0.765 0.444575
onset_prev -5.130e-05 5.793e-05 -0.885 0.376476
slope_prev 1.077e-01 2.593e-02 4.152 4.08e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01125 on 373 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: 0.5243, Adjusted R-squared: 0.5014
F-statistic: 22.84 on 18 and 373 DF, p-value: < 2.2e-16
CRF predicting mean difference
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_meandiff_socdist <- cforest(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + cdu + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist %>% drop_na(),
controls = ctrls)
crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)
crf_meandiff_socdist_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Export data
us_list_results <- list(cox_onset_prev, cox_onset_prev_ctrl,
lm_slope_prev, lm_slope_prev_coef,
cox_cpt_socdist, cox_cpt_socdist_ctrl, cox_cpt_socdist_ctrl2,
lm_meandiff_socdist, lm_meandiff_socdist_ctrl, lm_meandiff_socdist_ctrl2)
results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl',
'lm_slope_prev', 'lm_slope_prev_coef',
'cox_cpt_socdist', 'cox_cpt_socdist_ctrl', 'cox_cpt_socdist_ctrl2',
'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl', 'lm_meandiff_socdist_ctrl2')
names(us_list_results) <- results_names
save(us_list_results, file="us_list_results.RData")
write_csv(df_us_slope_prev, 'df_us_slope_prev.csv')
write_csv(df_us_cpt_socdist, 'df_us_cpt_socdist.csv')
---
title: "COVID19 GER"
author: "Heinrich Peters"
date: "4/23/2020"
output: html_notebook
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

# MAC
 knitr::opts_knit$set(root.dir = '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/GER')
 
library(lmerTest)
library(nlme)
library(psych)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(party)

```

# Prepare data

### Prevalence
```{r}

df_ger_prev <- read_csv('Germany_timeseries_prep.csv')

df_ger_prev <- df_ger_prev %>% mutate(date = as.Date(date, "%d%b%Y"),
                                  kreis = as.character(kreis)) %>% 
  dplyr::select(kreis, date, rate_day)

df_ger_prev
```

### Scoial distancing
```{r}

df_ger_socdist <- read_csv('Germany_socdist_fb_kreis.csv')

df_ger_socdist <- df_ger_socdist %>% 
  rename(socdist_single_tile = all_day_ratio_single_tile_users) %>%
  select(kreis, date, socdist_single_tile) %>% 
  mutate(kreis = as.character(kreis))

df_ger_socdist

```

### Personality 
```{r}

df_ger_pers <- read_csv('Germany_timeseries_prep.csv')

df_ger_pers <- df_ger_pers %>% 
  select(kreis, open, sci, extra, agree, neuro) %>%
  dplyr::rename(pers_o = open, 
         pers_c = sci,
         pers_e = extra,
         pers_a = agree,
         pers_n = neuro) %>%
  distinct() %>%
  mutate(kreis = as.character(kreis))

df_ger_pers

```


### Controls 
```{r}

df_ger_ctrl <- read.csv2('Germany_controls.csv', sep = ';', dec=',')

df_ger_ctrl <- df_ger_ctrl %>% select(-kreis_nme) %>%
    mutate(kreis = as.character(kreis),
           popdens = popdens %>% 
             as.character() %>%
             str_replace('\\.', '')%>%
             as.numeric())

df_ger_ctrl

```

### Merge prevalence data 
```{r}
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_prev$date),
                          max(df_ger_prev$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')


# merge prevalence data
df_ger_prev <- df_ger_prev %>% 
  inner_join(df_ger_pers, by = 'kreis') %>%
  inner_join(df_ger_ctrl, by = 'kreis') %>%
  merge(df_dates, by='date') %>% 
  arrange(kreis)

df_ger_prev
```

### Merge socdist data
```{r}

# create sequence of dates
date_sequence <- seq.Date(min(df_ger_socdist$date),
                          max(df_ger_socdist$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge socdist data
df_ger_socdist <- df_ger_socdist %>% 
  inner_join(df_ger_pers, by = 'kreis') %>%
  inner_join(df_ger_ctrl, by = 'kreis') %>% 
  merge(df_dates, by='date') %>% 
  arrange(kreis)

df_ger_socdist

```

### Control for weekend effect 
```{r}

easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)

df_ger_loess <- df_ger_socdist %>% 
  mutate(weekday = format(date, '%u')) %>% 
  filter(!(weekday %in% c('6','7') | date %in% easter)) %>% 
  split(.$kreis) %>%
  map(~ loess(socdist_single_tile ~ time, data = .)) %>%
  map(predict, 1:max(df_ger_socdist$time)) %>% 
  bind_rows() %>% 
  gather(key = 'kreis', value = 'loess') %>% 
  group_by(kreis) %>% 
  mutate(time = row_number())

df_ger_socdist <- df_ger_socdist %>% merge(df_ger_loess, by=c('kreis', 'time')) %>% 
  mutate(weekday = format(date, '%u')) %>% 
  mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter, 
                                            loess, socdist_single_tile)) %>%
  arrange(kreis, time) %>% 
  select(-weekday)

df_ger_socdist %>% drop_na()

```


# Explore data

### Plot prevalence over time
```{r}

df_ger_prev %>% ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_ger_prev %>% mutate(prev_tail = cut(.[[i]], 
      breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
      labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```


### Plot social distancing (single tile) over time

```{r}

df_ger_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_ger_socdist %>% mutate(prev_tail = cut(.[[i]], 
      breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
      labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```


```{r}

df_ger_socdist <- df_ger_socdist %>% mutate(socdist_single_tile = socdist_single_tile_clean) %>% 
  select(-loess, -socdist_single_tile_clean)

```


### Correlations
```{r}

df_ger_prev %>% group_by(kreis) %>% 
  summarize_if(is.numeric, mean) %>% 
  select(-kreis, -time) %>% 
  cor(use='pairwise.complete') %>% 
  round(3) %>% as.data.frame()

df_ger_socdist %>% group_by(kreis) %>% 
  summarize_if(is.numeric, mean) %>% 
  select(-kreis, -time) %>% 
  cor(use='pairwise.complete') %>% 
  round(3) %>% as.data.frame()
 
 
```

## Rescale data
```{r}

lvl2_scaled <- df_ger_prev %>% 
  dplyr::select(-time, -rate_day, -date) %>% 
  distinct() %>% 
  mutate_at(vars(-kreis), scale)
  
lvl1_scaled <- df_ger_prev %>% select(kreis, time, rate_day)

df_ger_prev_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')

df_ger_prev_scaled

```

```{r}

lvl2_scaled <- df_ger_socdist %>% 
  dplyr::select(-time, -socdist_single_tile, -date) %>% 
  distinct() %>% 
  mutate_at(vars(-kreis), scale)
  
lvl1_scaled <- df_ger_socdist %>% select(kreis, time, socdist_single_tile)

df_ger_socdist_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')

df_ger_socdist_scaled

```

# Predict Prevalence
### Extract first day of covid outbreak
```{r}

# get onset day
df_ger_onset_prev <- df_ger_prev_scaled %>% 
  group_by(kreis) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  summarize(onset_prev = min(time))
  
# merge with county data
df_ger_onset_prev <- df_ger_prev_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  left_join(df_ger_onset_prev, by = 'kreis')

# handle censored data
df_ger_onset_prev <- df_ger_onset_prev %>% 
  mutate(event = ifelse(is.na(onset_prev), 0, 1)) %>% 
  mutate(onset_prev = replace_na(onset_prev, as.numeric(diff(range(df_ger_prev$date)))+1))

df_ger_onset_prev

```

### Extract slopes
```{r}

# cut time series before onset
df_ger_prev_scaled <- df_ger_prev_scaled %>% 
  group_by(kreis) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  mutate(time = time-min(time)+1) %>%
  ungroup() %>%
  filter(time <= 30) %>%
  select(-rate_cs)

# drop counties with little data
df_ger_prev_scaled <- df_ger_prev_scaled %>%
  group_by(kreis) %>%
  filter(n() == 30) %>%
  ungroup()

# extract slope prevalence
df_ger_slope_prev <- df_ger_prev_scaled %>% 
  split(.$kreis) %>% 
  map(~ lm(rate_day ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('kreis') %>% 
  rename(slope_prev = '.')

# merge with county data
df_ger_slope_prev <- df_ger_prev_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  inner_join(df_ger_slope_prev, by = 'kreis') %>%
  drop_na()

# standardize slopes
df_ger_slope_prev <- df_ger_slope_prev %>% 
  mutate(slope_prev = scale(slope_prev))

```


### Explore distributions
```{r}

df_ger_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()
df_ger_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()

```


## Predict COVID onset with time-to-event regression 
```{r}

# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~ 
                          pers_o + pers_c + pers_e + pers_a + pers_n, 
                        data = df_ger_onset_prev)
cox_onset_prev %>% summary()

# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~ 
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                             data = df_ger_onset_prev)
cox_onset_prev_ctrl %>% summary()

```

## Predict prevalence slopes with linear models
```{r}

# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_ger_slope_prev)
lm_slope_prev %>% summary()
lm_slope_prev %>% confint(level=0.9)

# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                         data = df_ger_slope_prev)
lm_slope_prev_ctrl %>% summary()
lm_slope_prev_ctrl %>% confint(level=0.9)

```

### CRF predicting slopes
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_slope_prev <- cforest(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_slope_prev, 
                         controls = ctrls)

crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)

crf_slope_prev_varimp
crf_slope_prev_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
crf_slope_prev_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

## Predict Social Distancing
### Change point analysis
```{r}

# keep only counties with full data
kreis_complete <- df_ger_socdist_scaled %>% 
  group_by(kreis) %>% 
  summarize(n = n()) %>% 
  filter(n==max(.$n)) %>% 
  .$kreis

# run changepoint analysis
df_ger_socdist_cpt_results <- df_ger_socdist_scaled %>% 
  select(kreis, socdist_single_tile) %>%
  filter(kreis %in% kreis_complete) %>% 
  split(.$kreis) %>%
  map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
                    #penalty = 'Asymptotic',
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1,
                    test.stat = 'Normal'))

# calculate change point
df_ger_socdist_cpt_day <- df_ger_socdist_cpt_results %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_socdist = '.') %>%
  rownames_to_column('kreis')

# calculate mean differences
df_ger_socdist_cpt_mean_diff <- df_ger_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_socdist = '.') %>%
  rownames_to_column('kreis')

# calculate varaince differences
df_ger_socdist_cpt_var_diff <- df_ger_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$variance) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(var_diff_socdist = '.') %>%
  rownames_to_column('kreis')

# merge with county data
df_ger_cpt_socdist <- df_ger_socdist_scaled %>% 
  select(-time, -socdist_single_tile) %>%
  distinct() %>% 
  left_join(df_ger_socdist_cpt_day, by='kreis') %>%
  left_join(df_ger_socdist_cpt_mean_diff, by='kreis') %>%
  left_join(df_ger_socdist_cpt_var_diff, by='kreis') %>%
  left_join(select(df_ger_onset_prev, kreis, onset_prev), by='kreis') %>%
  left_join(select(df_ger_slope_prev, kreis, slope_prev), by='kreis') 

# standardize mean/var differences
df_ger_cpt_socdist <- df_ger_cpt_socdist %>% 
  mutate(mean_diff_socdist = scale(mean_diff_socdist),
         var_diff_socdist = scale(var_diff_socdist))

# handle censored data
df_ger_cpt_socdist <- df_ger_cpt_socdist %>% 
  mutate(cpt_day_socdist = ifelse(is.na(cpt_day_socdist), 
                                  as.numeric(diff(range(df_ger_socdist$date))), 
                                  cpt_day_socdist)) %>% 
  mutate(event = ifelse(cpt_day_socdist >= 60, 0, 1))

```

```{r}
df_ger_cpt_socdist$cpt_day_socdist %>% hist()
df_ger_cpt_socdist$mean_diff_socdist %>% hist()
df_ger_cpt_socdist$var_diff_socdist %>% hist()

```

```{r}

for(i in head(df_ger_socdist_cpt_results, 5)){
  plot(i)
}

```


# Predicting change points with time-to-event regression 
```{r}

# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~ 
                           pers_o + pers_c + pers_e + pers_a + pers_n, 
                  data = df_ger_cpt_socdist)
cox_cpt_socdist %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~ 
                                pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_cpt_socdist,)
cox_cpt_socdist_ctrl %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl2 <- coxph(Surv(cpt_day_socdist, event) ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                  data = df_ger_cpt_socdist)
cox_cpt_socdist_ctrl2 %>% summary()

```

### Linear models predicting mean differences
```{r}

lm_meandiff_socdist <- lm(mean_diff_socdist ~ 
                            pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_ger_cpt_socdist)
lm_meandiff_socdist %>% summary()
lm_meandiff_socdist %>% confint(level=0.9)

lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_cpt_socdist,)
lm_meandiff_socdist_ctrl %>% summary()
lm_meandiff_socdist_ctrl %>% confint(level=0.9)

lm_meandiff_socdist_ctrl2 <- lm(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                  data = df_ger_cpt_socdist)
lm_meandiff_socdist_ctrl2 %>% summary()
lm_meandiff_socdist_ctrl2 %>% confint(level=0.9)

```

### CRF predicting mean difference
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_meandiff_socdist <- cforest(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                              data = df_ger_cpt_socdist %>% drop_na(),
                         controls = ctrls)

crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)

crf_meandiff_socdist_varimp
crf_meandiff_socdist_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

### Export data 
```{r}
us_list_results <- list(cox_onset_prev, cox_onset_prev_ctrl, 
     lm_slope_prev, lm_slope_prev_coef, 
     cox_cpt_socdist, cox_cpt_socdist_ctrl, cox_cpt_socdist_ctrl2,
     lm_meandiff_socdist, lm_meandiff_socdist_ctrl, lm_meandiff_socdist_ctrl2)

results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl', 
     'lm_slope_prev', 'lm_slope_prev_coef', 
     'cox_cpt_socdist', 'cox_cpt_socdist_ctrl', 'cox_cpt_socdist_ctrl2',
     'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl', 'lm_meandiff_socdist_ctrl2')

names(us_list_results) <- results_names

save(us_list_results, file="us_list_results.RData")

```

```{r}
write_csv(df_us_slope_prev, 'df_us_slope_prev.csv')
write_csv(df_us_cpt_socdist, 'df_us_cpt_socdist.csv')

```